In January 2026, the international scientific publishing house “Springer” published an English-language monograph “Nexus of Sustainability: Understanding of FEWSE Systems II” (Volume II), which became the result of active collaboration between scientists of the National Academy of Sciences of Ukraine and the International Institute for Applied Systems Analysis (IIASA; Laxenburg, Austria).
The results included in the monograph were mainly obtained in the 4th phase of the implementation of two joint projects of the NAS of Ukraine and IIASA:
- “Comprehensive analysis of robust preventive and adaptive measures of food, energy, water and social management in the context of systemic risks and consequences of COVID-19” (the project is carried out at the Center for Evaluation of Scientific Institutions’ Activities and Scientific Support for Regional Development of Ukraine of the NAS of Ukraine);
- “Integrated robust modeling and management of food-energy-water-land use nexus for sustainable development” (the project is carried out at IIASA).
The monograph was published with the support of the Committee on Systems Analysis of the NAS of Ukraine and edited by the President of the National Academy of Sciences of Ukraine, Academician Anatoliy Zahorodniy, the First Vice-President of the NAS of Ukraine, Academician Vyacheslav Bohdanov, Deputy Director for Scientific and Organizational Work of the Institute of General Energy of the NAS of Ukraine, Doctor of Technical Sciences Artur Zaporozhets, and Researcher at the International Institute for Applied Systems Analysis (Laxenburg, Austria), Candidate of Physical and Mathematical Sciences Tetiana Yermolieva. The work became the 627th volume in the “Studies in Systems, Decision and Control” series and consists of a foreword and 28 chapters.
The foreword outlines the relevance of an interdisciplinary approach to studying sustainable development problems amid modern global challenges, including climate change, depletion of natural resources, increasing systemic risks, and socio-economic inequality. It presents the general concept of the monograph dedicated to the analysis of interconnected FEWSE (Food–Energy–Water–Social–Environmental) systems, encompassing food, energy, water, social, and environmental components of development. It emphasizes that the research object is a complex of interacting resource and socio-ecological systems, whose interdependence determines the resilience of modern economies and societies.
The main scientific approaches and methodological tools used in the research are also outlined, including stochastic optimization, machine learning methods, model scaling techniques, and robust estimation, as well as the importance of forming coherent policies and adaptive resource management strategies to ensure sustainable development at local, national, and global levels.
Chapter 1 “Integrated Solutions to Food–Energy–Water–Environmental NEXUS Security Modeling and Management: Robust Downscaling and Models’ Linkage Procedures” (authors – President of the National Academy of Sciences of Ukraine Academician Anatoliy Zahorodniy, First Vice-President of the NAS of Ukraine Academician Vyacheslav Bohdanov, IIASA Researcher Candidate of Physical and Mathematical Sciences Tetiana Yermolieva, IIASA Senior Researcher Dr. Nadezhda Komendantova, Head of the IIASA Rural Development Center and Acting Director of the IIASA Ecosystem Services and Management Program Dr. Petr Havlik) reviews modern methods of systems analysis, models, and modeling tools developed within the joint project of IIASA and NAS of Ukraine on integrated management of food, energy, water, and environmental system security (FEWES Nexus).
This chapter emphasizes the nature of systemic risks arising in interconnected food-energy-water-environmental systems and justifies the need for coordinated preventive (ex-ante) and adaptive (ex-post) solutions for managing such systems. Often, model linkages between systems and models with different spatial and temporal resolutions are implemented using downscaling and upscaling procedures based on optimization criteria—for example, cross-entropy principles for constructing coherent models with heterogeneous data.
The chapter also describes methods for reducing scale mismatches between scenarios, model input data, and scales required for policy analysis and implementation, including examples of combining sectoral and regional optimization models into integrated systems with shared constraints. This approach allows coordinating decisions without disclosing internal information of individual models, overcoming the problem of asymmetric information—a key issue for practical application of integrated Nexus system management models.
The work establishes a methodological foundation for building integrated models of comprehensive management of food-energy-water-environmental security, combining heterogeneous systems, scales, data, and models into a single solution for policy planning and decision-making under uncertainty and risks.
Chapter 2 “Integrated Modelling for Catastrophic and Systemic Risks Management: Robust Coherent Balance Between Ex-Ante and Ex-Post Measures” (authors – IIASA Researcher Candidate of Physical and Mathematical Sciences Tetiana Yermolieva, IIASA Senior Researcher Dr. Nadezhda Komendantova, Professor of Urban and Regional Planning Faculty of Istanbul Technical University Dr. Seda Kundak, Research Assistant of the same faculty Dr. Cihan Mert Sabah, Associate Professor of Civil and Environmental Engineering Faculty of Polytechnic University of Catalonia Dr. Marcel Gürlimann, Professor of the same faculty Dr. Nieves Lantada Sarsosa, and Head of Mathematical Methods of Operations Research Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Corresponding Member Pavlo Knopov) considers integrated approaches to managing catastrophic and systemic risks in complex interconnected socio-economic and natural systems.
The authors analyze the nature of systemic catastrophic risks caused by climate change, natural and man-made hazards, as well as economic and social shocks, and demonstrate that effective management of such risks requires a coordinated combination of preventive (ex-ante) measures with response and recovery (ex-post) strategies. They propose a concept of integrated modeling combining stochastic catastrophe models, economic constraints, resilience indicators, and decision support tools.
The chapter shows that investments in prior protective measures can significantly reduce expected losses and increase system resilience while reducing the burden on compensatory and recovery mechanisms after risk realization. The results confirm the feasibility of applying integrated models to form effective risk management policies in food-energy-water-environmental and social systems under high uncertainty and growing global challenges.
Chapter 3 “Integrating EPIC-Based Meta-Models into GLOBIOM for Systemic Risks Management” (authors – IIASA Researcher Candidate of Physical and Mathematical Sciences Tetiana Yermolieva, IIASA Program Director Dr. Petr Havlik, IIASA Senior Researcher Dr. Stefan Frank, IIASA Researcher Dr. Andrey Lessa-Dersy-Augustinchik, IIASA Senior Researcher and Group Leader Dr. Taher Kahil, IIASA Senior Researcher Dr. Juraj Balkovic, IIASA Senior Researcher Dr. Rastislav Skalski, IIASA Senior Researcher Dr. Nadezhda Komendantova, and Head of Intelligent Information Technologies Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Doctor of Physical and Mathematical Sciences Vasyl Horbachuk) propose methods for integrating biophysical meta-models into the multisectoral land-use model GLOBIOM to assess and manage systemic risks in food, land use, and environmental systems under climate change.
The authors developed meta-models based on the agroecological EPIC model that simulate crop yields, soil element balances, and biophysical responses under variable climatic conditions and demonstrated how these meta-models can be embedded into the global land-use and food market model GLOBIOM. This integration allows combining biophysical assessments (yield, soil quality) with socio-economic production, trade, and land-use scenarios, considering uncertainties in climate and market factors.
The chapter emphasizes that implicit (stochastic) distributions of yield and soil conditions generated by meta-models are critical for two-stage stochastic optimization in GLOBIOM, integrating strategic ex-ante and adaptive ex-post resource management decisions. The authors present numerical results showing that changes in preventive measure costs (production costs, investments, subsidies) can significantly affect food production, management technologies, and natural resource use, thereby impacting food, landscape, and environmental security.
Chapter 4 “Risk Measures and Distributionally Robust Optimization for Decision Making Under Uncertainty” (author – Leading Researcher of Mathematical Methods of Operations Research Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Doctor of Physical and Mathematical Sciences Volodymyr Kyryliuk) develops and analyzes methodological approaches to risk assessment and decision optimization under uncertainty, especially uncertainty about input data distributions.
The chapter systematically justifies a class of functional risk measures, including Conditional Value-at-Risk (CVaR) and other stochastic estimates applicable for quantitative risk assessment in multisectoral Nexus systems—food-energy-water-environmental and socio-economic complexes. Special attention is given to Distributionally Robust Optimization (DRO), which formulates optimization problems so that their solutions remain effective and stable even under uncertainty about input parameter distributions.
Methods for formulating and solving DRO problems, where uncertainty is described by families of distributions, are proposed to provide more reliable risk management solutions in complex systems. Applications in strategic resource planning, investment decisions, and policy recommendations for ensuring resilience of food-energy-water-environmental systems under uncertainty and global risks are illustrated.
These approaches are crucial for forming adaptive risk management policies that account for both internal stochasticity of processes and external data uncertainty, especially important for informed decision-making in intersectoral and cross-industry contexts.
Chapter 5 “Optimal Control Problems Under Risk and Uncertainty” (authors – researchers of Mathematical Methods of Operations Research Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine: Head of Department Corresponding Member Pavlo Knopov, Senior Researcher Candidate of Physical and Mathematical Sciences Tetiana Pepeliaieva, Junior Researcher PhD Oleksandr Bohdanov, and Junior Researcher Serhii Shpyha) consider new approaches to optimal control problems of dynamic stochastic systems under uncertainty.
This chapter investigates and justifies the construction of adequate risk estimates and the search for optimal decision-making strategies for a wide class of applied problems where the control object is described by dynamic stochastic processes or fields (e.g., Markov processes), including those arising in economics, technology, ecology, finance, and insurance.
The authors propose a systematic methodology for optimal control under risk and uncertainty, including analysis of stochastic differential models, formulation and solution of control problems considering parameter uncertainty and external influences, and the search for strategies minimizing long-term risks and ensuring effective system functioning in uncertain environments.
The chapter’s results are significant for mathematical support of decision-making in complex systems where uncertainty and risk are integral to process dynamics and can be used for further development of stochastic control theory in applied sciences and policy planning under modern challenges.
Chapter 6 “Methods of Using a Priori Information in Statistics” (authors – researchers of V.M. Glushkov Institute of Cybernetics NAS of Ukraine: Head of Mathematical Methods of Operations Research Department Corresponding Member Pavlo Knopov, Professor of Prydniprovska State Academy of Civil Engineering and Architecture Doctor of Physical and Mathematical Sciences Arnold Korkhin, and Associate Professor of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Candidate of Physical and Mathematical Sciences Liliya Vovk) consider statistical methods that allow incorporating a priori information when estimating parameters of statistical models, especially in regression modeling and forecasting tasks.
The authors focus primarily on methods for estimating linear regression parameters using a priori information, as this is a fundamental statistical task and a special case of estimating mathematical expectation. The chapter describes the Bayesian approach for estimating regression parameters and variance of the random component under various assumptions, as well as methods for constructing both unbiased and biased estimates that improve estimation accuracy and forecasting quality.
Methods for estimating variable regression parameters, including building switching regressions with unknown switching points, important for modeling systems with different behavior modes, are separately considered. These approaches can be used for processing heterogeneous data, adapting models to structural changes, and enhancing reliability of forecast estimates under uncertainty typical for complex food-energy-water-social-environmental (FEWSE) systems.
Chapter 7 “Models Parametric Analysis via Adaptive Kernel Learning” (authors – Leading Researcher of Mathematical Methods of Operations Research Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Doctor of Physical and Mathematical Sciences Volodymyr Norkin and Dean of Mathematics Faculty of Chemnitz Technical University (Germany) Professor Dr. Alois Pichler) consider methods of parametric analysis of mathematical models based on adaptive kernel learning—a modern machine learning approach applied to estimate dependencies of complex models on their parameters.
This chapter proposes a methodology starting with finding the probability distribution of model parameters and forming a training sample by selective solving of the original problem for each parameter set. Then, based on the obtained data, kernel approximation is applied, minimizing approximation error on the training sample and considering kernel function shapes to enhance model flexibility.
The main feature of the approach is extending the traditional use of Support Vector Machine (SVM) methods in Reproducing Kernel Hilbert Spaces (RKHS) to a broader class of functions with L₂ norm regularization, allowing adaptation not only of weights but also kernel function shapes for better capturing complex dependencies in parameter space. The authors also propose procedural steps for selecting the optimal regularization parameter based on minimizing error on the test sample.
The proposed methodology is important for analyzing parametric uncertainty in integrated models of complex systems, particularly in food-energy-water-environmental (FEWSE) systems, as it enables quantitative assessment of parameter variation impact on model behavior, improving accuracy and reliability of forecast estimates in complex scenarios.
Chapter 8 “Program-Targeted Approach for Improving Energy Efficiency and Sustainability Through System-Integrated Services” (authors – researchers of Institute of General Energy NAS of Ukraine: Senior Researcher of Fuel and Energy Complex Structure Transformation Department Candidate of Technical Sciences, Associate Professor Tetiana Yevtukhova, Head of the same department Corresponding Member of NAS of Ukraine Oleksandr Novoseltsev, and Deputy Director for Scientific and Organizational Work of the Institute Doctor of Technical Sciences Artur Zaporozhets) propose a program-targeted approach to improving energy efficiency and sustainability through integrated services within energy programs.
This chapter studies the structure and logic of interaction among initiators, participants, implementers, and investors of energy efficiency and energy sustainability programs implemented at regional, national, and local levels. The authors justify an algorithm for optimal coordination of actions of all stakeholders and propose a formalized simulation model enabling testing of various project selection strategies in sequential iterative procedures based on Bellman’s optimality principle.
It is shown that through sequential optimal project selection and balancing joint efforts, technical, economic, and energy conditions for program implementation can be more effectively coordinated, as well as increasing the pace and scale of energy-efficient solution deployment. The use of dynamic programming to solve multidimensional optimization problems reduced to sequential one-dimensional tasks is illustrated, significantly simplifying calculations and improving accuracy of energy-saving potential and greenhouse gas emission reduction assessments.
The results contribute to forming more effective energy efficiency and energy sustainability programs, enhancing interaction among energy market participants, and increasing economic and environmental efficiency of energy services in the context of implementing the UN Sustainable Development Goals.
Chapter 9 “To Computable Decentralized Heterogeneous Energy Market Supply Model” (authors – Head of Intelligent Information Technologies Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Doctor of Physical and Mathematical Sciences Vasyl Horbachuk, Senior Researcher of Scientific Support and Innovation Monitoring Department of the Center for Evaluation of Scientific Institutions’ Activities and Scientific Support for Regional Development of Ukraine NAS of Ukraine Candidate of Technical Sciences Serhii Shulzhenko, and Head of High-Performance and Distributed Computing Laboratory of Programming Automation Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine Candidate of Technical Sciences Andrii Holovynskyi) propose a computable model of a decentralized heterogeneous energy supply market considering modern trends of distributed generation and integration of renewable energy sources.
This chapter discusses that the traditional electricity market, where demand and supply balance is ensured by centralized network participants, loses stability in the modern distributed energy environment integrating numerous small-scale renewable generation sources, storage systems, and ICT-based services. The authors formulate an approach to building a computable market model that balances demand and supply at local network levels and provides an intelligent platform for energy management, integrating both physical electricity flows and information services for decision-making.
The developed model accounts for distributed generation characteristics, market participant heterogeneity, and digital interactions, facilitating effective energy balance management amid unpredictable demand and supply changes. This enables not only maintaining stability of local energy systems but also improving renewable integration, increasing market flexibility, and reducing balancing costs. The chapter’s results are important for developing intelligent energy market platforms and distributed energy supply models addressing challenges of modern energy transformation.
Chapter 10 “Perspectives from Life Cycle Analysis: Hydrogen Production Outlook and Contribution to SDGs” (authors – IIASA Senior Researcher Dr. Nadezhda Komendantova, UNIDO Research and Policy Specialist Dr. Smitha Fokir, and IIASA Researcher Candidate of Physical and Mathematical Sciences Tetiana Yermolieva) analyze prospects of hydrogen production as a key technology for energy transformation and achieving the UN Sustainable Development Goals (SDGs) using Life Cycle Analysis (LCA) approaches.
The chapter considers current and prospective low-carbon hydrogen production technologies, including hydrogen electrolysis, methane pyrolysis, thermochemical cycles, and others, and evaluates their environmental and socio-economic life cycle indicators (LCA indicators) under high data uncertainty and technological development.
The authors emphasize that while transitioning to a green hydrogen economy is an important component of energy system decarbonization, significant risks and uncertainties exist: water resource limitations for hydrogen production, high investment costs, technological immaturity of some processes, and regulatory barriers.
The chapter also reviews LCA literature on hydrogen technologies, highlighting the potential contribution of low-carbon hydrogen to SDGs, considering not only emission reductions but also socio-economic aspects—including costs, employment, resource impacts, and integration into intersectoral systems (energy-food-water).
The chapter’s conclusions support forming integrated approaches to assessing and planning hydrogen economy implementation, which can underpin policies supporting decarbonization, renewable energy development, and sustainable development in international and national contexts.
Chapter 11 “Long-Term Photovoltaic Power Generation Forecasting Using Long Short-Term Memory Network: Insights into Stochastic Dynamics” (authors – Researcher of Energy Efficiency Forecasting and Advanced Fuel and Energy Balances Department of Institute of General Energy NAS of Ukraine PhD Dmytro Matushkin, Deputy Director for Scientific and Organizational Work of the Institute Doctor of Technical Sciences Artur Zaporozhets, Head of the same department Candidate of Technical Sciences Valentyna Stanitsyna, and Deputy Director for Scientific and Organizational Work of G.E. Pukhov Institute for Modeling in Energy NAS of Ukraine Doctor of Technical Sciences Volodymyr Artemchuk) develop methods for long-term forecasting of photovoltaic (PV) electricity generation considering stochastic dynamics of weather conditions and solar generation parameters.
This chapter uses Long Short-Term Memory (LSTM) neural networks—a modern machine learning approach capable of effectively modeling time series with nonlinearities and stochastic fluctuations. The authors explain how PV generation modeling based on real meteorological data improves forecast accuracy for horizons up to several months, especially in the context of integrating distributed renewable energy sources into modern energy systems.
Practical results demonstrate high forecasting quality (high correlation between predicted and actual values), indicating LSTM networks’ ability to adequately reproduce complex dependencies between meteorological parameters and PV energy production. This is important for planning and balancing energy systems, supporting stable operation of distributed networks, and integrating large shares of renewables, especially amid growing uncertainty in electricity demand and supply.
Chapter 12 “Second-Life EV Batteries Application for Energy Storage: Global Trends, Policies and Technologies” (authors – researchers of Institute of General Energy NAS of Ukraine: Researcher of Electric Power Complex Development Forecasting Department Hanna Kostenko, Director of the Institute Academician Vitaliy Babak, and Deputy Director for Scientific and Organizational Work Doctor of Technical Sciences Artur Zaporozhets) consider current trends, policies, and technologies for using electric vehicle (EV) batteries in secondary applications as components of energy-saving energy storage systems.
This chapter analyzes the global context of the phenomenon of second-life use of spent lithium-ion EV batteries, which, after exhausting active service resources in transport, still have significant potential for stationary applications, including energy storage from renewables and strengthening energy infrastructure. The authors emphasize that repurposing such batteries extends their life cycle, reduces electronic waste, and promotes circular economy principles while decreasing the need for new resource extraction and environmental impact.
Special attention is given to political and technological aspects of this practice at the international level, including modernization of regulatory frameworks encouraging repurposing EV batteries for stationary energy storage systems and integrating such systems into grids with high shares of renewable generation. The chapter also reviews key technical challenges and solutions—from battery health assessment to management and safety system requirements—ensuring reliable operation of secondary batteries in complex energy environments.
The authors conclude that a combined approach to reuse and recycling of EV batteries is an optimal strategy for sustainable resource management and supporting energy transformation, and development of relevant technologies and policies is key to enhancing energy security and environmental sustainability.
Chapter 13 “Financial Model of Operation of Distributed Generation Power Plants with Energy Storage System in Synchronous and Isolated Mode” (authors – researchers of Institute of General Energy NAS of Ukraine: Researcher of Electric Power Complex Development Forecasting Department PhD Ihor Buratynskyi, Leading Researcher of the same department Candidate of Technical Sciences Tetiana Nechaeva, and Deputy Director for Scientific and Organizational Work Doctor of Technical Sciences Artur Zaporozhets) consider a financial model of operation of distributed energy generation units combined with energy storage systems (ESS) under synchronous and isolated operation modes of the energy node.
This chapter formulates a mathematical problem of balancing electricity demand and supply for an energy node with capacity up to 4 MW, including distributed generation units (wind, solar), a backup gas power plant, and ESS. An approach to evaluating financial flows determining production cost, market participant revenues, and costs of system implementation and operation in two modes is developed:
– synchronous mode, where electricity is sold on the day-ahead market, and the ESS operator provides ancillary services for frequency and active power regulation;
– isolated mode, where the node fully supplies consumption with its own resources, and electricity cost is formed considering all production and storage expenses.
Numerical estimates show that switching from synchronous to isolated mode increases the discounted payback period of the distributed generation system with ESS from about 5.5 years to 8.7 years. This allows critical assessment of economic efficiency of such complexes under various operational conditions and serves as a tool for making informed investment decisions on implementing distributed renewable and storage technologies in energy.
Chapter 14 “Smart Micro-Grids of Renewable Energy as an Element of the Nexus of Sustainable Development” (authors – researchers of Institute of Economics and Forecasting NAS of Ukraine: Deputy Director for Scientific Work Academician Andriy Hrytsenko and Leading Researcher of Digital Economy Sector of Economic Theory Department Doctor of Economics, Professor Volodymyr Lypov) study features of development of “smart” renewable energy microgrids as an important element of complex sustainable development systems “energy–water–food–society–environment.”
This chapter reveals organizational forms, advantages, critical factors, and incentive mechanisms for microgrid development capable of functioning as local energy subsystems with intelligent energy flow management, surplus generation storage, and quality assurance. The authors emphasize the key role of smart microgrids in creating value chains at local community levels, stimulating economic activity, enhancing energy security, and reducing dependence on centralized energy systems.
Main directions of smart grid functioning—from energy flow management and energy storage to service organization, energy market interaction, and integration with national systems—are analyzed. The authors demonstrate that such microgrids contribute to increasing ecological stability of territories, shortening equipment payback periods, expanding community decision-making capabilities on energy self-sufficiency, and accelerating transition to a decarbonized and democratic energy paradigm.
These results highlight the importance of smart renewable energy microgrids as a component on the path to achieving the UN Sustainable Development Goals, as they combine technical, economic, and social aspects of energy transformation at local community and national energy system levels.
Chapter 15 “Social Resilience of the Ukrainian Economy for Post-war Recovery, Solidarization and Sustainable Development” (authors – researchers of Institute of Economics and Forecasting NAS of Ukraine: Director Academician Valeriy Heyets, Leading Researcher of Institutional Economics Sector of Economic Theory Department Doctor of Economics, Associate Professor Tetiana Burlay, and Head of Socioeconomic Labor Department Corresponding Member Viktoriya Blyzniuk) analyze social resilience of Ukraine’s economy in the context of post-war recovery, societal solidarization, and sustainable development.
This chapter substantiates a conceptual model of social resilience of the national economy based on three key components: employment and labor potential, solvent consumer demand, and social support for the population. The authors emphasize that multidimensional social resilience is critical for Ukraine to withstand large-scale military shocks, ensure effective post-war recovery, and contribute to achieving the UN Sustainable Development Goals by 2030.
Main social risks threatening post-war stability of Ukrainian society are studied, including further development of demographic crisis, rising poverty levels, social environment differentiation due to mass forced migrations, widespread illegal arms proliferation in post-war Ukraine, and insufficient veteran adaptation to civilian life. The authors stress the necessity of developing and implementing state policies aimed at minimizing these threats and strengthening the country’s social resilience.
The chapter’s results can serve as a basis for forming social policy strategies and support programs for stable recovery of Ukraine’s economy, improving citizens’ quality of life, and strengthening food-energy-water-social-environmental (FEWSE) security amid modern challenges.
Chapter 16 “Modeling and Forecasting of Structural Development Factors and Priority Types of Economic Activity in Post-war Ukraine” (authors – researchers of Institute of Economics and Forecasting NAS of Ukraine: Director Academician Valeriy Heyets and Chief Researcher Acting Head of Economic Development Modeling and Forecasting Department Corresponding Member Mariya Skrypnychenko) consider modeling and forecasting structural development factors and priority economic activity types for post-war economic recovery of Ukraine.
This chapter formulates an extended production function including integral indicators of structural factors, enabling quantitative assessment of the Russian Federation’s military aggression impact on Ukraine’s socio-economic dynamics. Based on this model, alternative scenario forecasts (Baseline Scenario/EFF and Downside Scenario/EFF) of macroeconomic indicators are built considering various external financial aid options, including IMF forecasts, and priority development trends—innovative-technological and humanitarian—as well as key “determinant sectors” of the post-war economy (defense-industrial complex, agriculture, construction, trade) are substantiated.
Special attention is paid to diagnosing structural imbalances and forming strategic directions of economic policy facilitating transition from a traditional raw-material-agricultural, import-dependent model to a model based on science achievements, high technologies, and new technological order. Based on this, proposals for state policy priorities for sustainable development of Ukraine’s economy in the near future (up to 2027) are developed.
These results are important for forming substantiated macroeconomic planning strategies, adaptive recovery mechanisms, and policies stimulating driving sectors of the economy to ensure Ukraine’s resilience and competitiveness in the post-war period.
Chapter 17 “Modern Challenges of Planning and Modeling the Post-war Reconstruction of Ukraine’s Agriculture Based on the UN Integrated Human Rights Approach” (authors – Head of Department of Agricultural Transformation Economics and Policy Institute of Economics and Forecasting NAS of Ukraine Academician Olena Borodina, IIASA Researcher Candidate of Physical and Mathematical Sciences Tetiana Yermolieva, Leading Researcher of the same department Candidate of Economic Sciences Viktor Yarovyi, and Researcher of the same department Candidate of Economic Sciences Oleksii Frayer) analyze modern challenges of planning and modeling post-war reconstruction of Ukraine’s agriculture considering an integrated human rights-oriented approach in the context of the UN Sustainable Development Cooperation Framework (UNSDCF).
The authors review national models of sustainable development of the agricultural sector and rural areas jointly developed by the Institute of Economics and Forecasting NAS of Ukraine and IIASA and justify the need to integrate a human rights and sustainable development value-based approach into resource allocation, land restoration, and food security during post-war reconstruction.
The chapter highlights key problems hindering agricultural sector recovery, including discriminatory land market conditions, insufficient support for small farmers, soil contamination due to hostilities, and European integration challenges. The authors propose expanding national planning models combining economic, environmental, and social indicators by integrating human rights and sustainable development principles. This will promote equitable resource access, sustainable land use, and long-term agricultural recovery.
The obtained results can serve as a basis for developing policies and strategies ensuring resilience of Ukraine’s agricultural sector, food security, and socio-economic stability in the post-war period, aligning national priorities with the UN Sustainable Development Goals.
Chapter 18 “Fuzzy Cognitive Maps in Corn Yield Forecast” (authors – Chief Researcher of Geo-Information Technologies in Remote Sensing of Earth Department of Aerospace Research Center of Earth Institute of Geological Sciences NAS of Ukraine Doctor of Technical Sciences Svitlana Kokhan, Director of this Center Corresponding Member Mykhailo Popov, Associate Professor of Aerospace Geodesy and Land Management Department of Faculty of Architecture, Construction and Design National Aviation University Candidate of Technical Sciences Sofiya Alpert, Junior Researcher of Geo-Information Technologies Department of Aerospace Research Center of Earth Institute of Geological Sciences NAS of Ukraine PhD Artem Andreev, Senior Researcher of this department Candidate of Technical Sciences Oleh Drozdivskyi, PhD student of Technical Means of Remote Sensing Laboratory of this department Yuliya Temna, Junior Researcher of this laboratory Oksana Sybirtseva, and Leading Engineer of the laboratory Yelyzaveta Dorofey) propose a method for forecasting corn yield based on fuzzy cognitive maps (FCM), combining expert knowledge and quantitative data to model influences of key factors on yield.
This chapter develops an FCM model where nodes represent main factors affecting corn yield (climatic parameters, soil properties, vegetation indices), and weighted directed edges show the strength of these factors’ influence on the final yield indicator. The authors analyze the effectiveness of selected predictors and show that climatic and soil indicators (FPAR, LST, SSM) provide relative forecast errors of 5.1–10%, while vegetation state indices (LAI, NDVI, MSAVI) yield acceptable errors of 10.1–15%, corresponding to typical agro-practice requirements.
The chapter’s results have important applied significance for crop yield forecasting systems adapted to climate change and natural condition variability and can be used for agricultural production planning, food security assessment, and decision support in the agricultural sector amid modern global climate change challenges.
Chapter 19 “Assessment of the Socio-Natural Development of Ukraine Oblasts in the Conditions of Modern Challenges” (authors – Head of Energy and Mass Exchange in Geosystems Department of Aerospace Research Center of Earth Institute of Geological Sciences NAS of Ukraine Candidate of Geographical Sciences Lesya Yelistratova, Researcher of the same department Candidate of Geological Sciences Oleksandr Apostolov, Director of the Center for Evaluation of Scientific Institutions’ Activities and Scientific Support for Regional Development of Ukraine NAS of Ukraine Candidate of Physical and Mathematical Sciences Oleksandr Bakhonskyi, Scientific Secretary of this Center Candidate of Chemical Sciences Halyna Kuzminska, Leading Engineer of Energy and Mass Exchange in Geosystems Department of Aerospace Research Center of Earth Institute of Geological Sciences NAS of Ukraine Maksym Tymchyshyn, and Senior Researcher of this department Candidate of Geological and Mineralogical Sciences Artur Khodorovskyi) comprehensively assess socio-natural development of Ukraine’s oblasts amid modern challenges based on calculating integral indices considering social, economic, and natural components.
The authors develop and apply a methodology for calculating the Socio-Natural Development Index (SDI), integrating data on economic indicators (GRP, population income, life expectancy) and biotic parameters (biotic state assessed by MODIS satellite data) for each administrative oblast of Ukraine. Statistical information from the State Statistics Service of Ukraine and satellite data on vegetation and local biota conditions are used for data processing.
The study results show that oblasts with higher economic development levels do not always have better natural conditions, indicating complex interactions of economic and ecological development factors. For example, according to the SDI, leaders include Zakarpattia, Ivano-Frankivsk, Lviv, Volyn, and others, while Donetsk, Kharkiv, Zaporizhzhia, Mykolaiv, and Kherson oblasts are characterized by low biotic development due to strong anthropogenic pressure. The chapter emphasizes the need to consider socio-ecological aspects in regional development and post-war reconstruction policies in Ukraine to balance economic and natural resource priorities.
Chapter 20 “Physics-Informed Neural Networks for Weak Solutions in Budyko–Sellers Climate Models” (authors – researchers of Educational and Scientific Complex “Institute of Applied Systems Analysis” of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ministry of Education and Science of Ukraine and NAS of Ukraine: Scientific Supervisor Academician Mykhailo Zgurovskyi, Director of the Complex Corresponding Member Pavlo Kasyanov, Junior Researcher PhD Lyudmyla Levenchuk, PhD student Vladyslav Novikov, and Senior Researcher Candidate of Physical and Mathematical Sciences Liliya Paliichuk) propose a new approach to modeling climate processes based on Physics-Informed Neural Networks (PINNs) for solving weak solutions in the Budyko–Sellers atmospheric energy balance model.
This chapter studies the Budyko–Sellers climate model describing the balance between absorbed solar radiation and emitted thermal energy on Earth’s surface. Due to complexity and nonlinearity of differential equations, classical numerical methods often cannot efficiently find generalized solutions. The authors propose a PINNs approach integrating physical laws directly into the neural network structure, enabling approximation of weak solutions of such problems.
The proposed methodology is demonstrated with numerical simulations confirming high accuracy and computational efficiency of PINNs compared to traditional approaches. The chapter’s results are significant for improving climate dynamics models, critically important for assessing climate change impacts and developing adaptive strategies in sustainable development and climate policy.
Chapter 21 “Gridded Observation-Based Climate Datasets of Ukraine” (authors – researchers of Ukrainian Hydrometeorological Institute of State Emergency Service of Ukraine and NAS of Ukraine: Director Academician Volodymyr Osadchyi, Senior Researcher of Atmospheric Physics Department Candidate of Geographical Sciences Olesya Skrynyk, Senior Researcher of the same department Candidate of Geographical Sciences Lyudmyla Palamarchuk, Senior Researcher of the same department PhD Vladyslav Sidenko, Senior Researcher of the same department Candidate of Geographical Sciences Dmytro Oshurok, First Category Engineer of Atmospheric Physics Department Ihor Kravchenko, and Senior Researcher of Atmospheric Physics Department Candidate of Physical and Mathematical Sciences Oleh Skrynyk) present developed gridded climate databases for Ukraine’s territory, which are an important scientific basis for climate monitoring and research, forecasting, and development and implementation of national climate change adaptation policy. The datasets contain monthly and daily values of atmospheric precipitation and near-surface air temperature (minimum, average, and maximum) covering 1946–2020 with spatial resolution ~10 km × 10 km (0.1° × 0.1°).
This chapter presents results of processing monthly and daily data from 178 meteorological stations in Ukraine, partially obtained from various paper sources. Data were processed according to World Meteorological Organization requirements and recommendations, including detailed quality control, climatological homogenization (detection and removal of station signals from time series), and gridding based on geospatial modeling. The result is creation of two key products: ClimUAm (monthly gridded temperature and precipitation series) and ClimUAd (daily gridded series), which have great applied and theoretical significance for further climate research, monitoring, modeling, and adaptation measure development in various economic and ecological sectors.
The presented results are important for building Ukraine’s scientific infrastructure in climate data and are a valuable resource for multidisciplinary research in the context of ensuring sustainable development of food-energy-water-social-environmental (FEWSE) systems.
Chapter 22 “Role of Natural Disasters and Anthropogenic Upheavals in Complex Atmosphere-Chemistry Interactions and Feedbacks” (authors – researchers of Ukrainian Hydrometeorological Institute of State Emergency Service of Ukraine and NAS of Ukraine: Head of Atmospheric Air Monitoring Laboratory of Atmosphere Monitoring Department Candidate of Geographical Sciences Mykhailo Savenets, Senior Researcher of Climate Change Impact on Water Resources Laboratory PhD Larysa Pysarenko, Senior Researcher of Atmospheric Air Monitoring Laboratory Candidate of Geographical Sciences Lyudmyla Nadtochiy, and Director of the Institute Academician Volodymyr Osadchyi) analyze the impact of natural and man-made emergencies on complex interactions between meteorological processes and atmospheric chemical composition in the context of modern environmental challenges.
This chapter considers a wide range of processes arising from natural disasters (forest fires, droughts) and anthropogenic impacts (explosions, military actions, technological catastrophes) leading to additional emissions of aerosol particles and other pollutants. The authors describe in detail the impact of aerosols on radiation balance, atmospheric temperature and humidity regimes, cloud formation, and how these processes may amplify climate change effects.
Special attention is given to analyzing changes in aerosol composition of atmospheric air after the destruction of the Kakhovka Hydroelectric Power Plant, disappearance of the Kakhovka Reservoir, and formation of new dust emission sources potentially affecting air quality in the region. The authors use a combination of remote sensing data and “seamless” numerical modeling to reveal relationships between aerosol components and meteorological condition changes.
The presented results are important for understanding consequences of natural and man-made emergencies on complex atmospheric processes, critically important for climate change modeling, ecological risk assessment, and development of adaptation measures to changing environmental conditions in Ukraine and worldwide.
Chapter 23 “Impact of Military Operations on the Hydrochemical Composition of Aquatic Ecosystems in Ukraine” (authors – Associate Professor of Physical Geography Department of Geography Faculty of Lesya Ukrainka Volyn National University Candidate of Geographical Sciences Valentyna Stelmakh, Associate Professor of the same department Candidate of Geographical Sciences Tetiana Pavlovska, Associate Professor of Ecology Department of Faculty of Agricultural Technologies and Ecology of Lutsk National Technical University Candidate of Geographical Sciences Vitalina Fedoniuk, Associate Professor of Physical Geography Department of Geography Faculty of Lesya Ukrainka Volyn National University Candidate of Biological Sciences Yurii Biletskyi, and Deputy Director for Scientific and Organizational Work of Institute of General Energy NAS of Ukraine Doctor of Technical Sciences Artur Zaporozhets) analyze the impact of military actions on Ukraine’s aquatic ecosystems through changes in hydrochemical composition of surface waters in rivers and lakes.
This chapter examines consequences of mass destruction of water supply and treatment infrastructure due to hostilities, including destruction of filtration stations, treatment facilities, dams, and reservoirs, leading to water quality degradation and disruption of natural hydrochemical processes. The impact of explosive devices, flooding of territories, damage to oil storage facilities, and ingress of technical fluids into river basins, including the Dnipro, Southern Bug, and other watercourses, is studied.
The authors analyze hydrochemical water parameters at control sites located in areas with varying intensity of hostilities and identify key changes in concentrations of chemical elements, pollution indicators, and other water quality markers. Based on the data, priority practical recommendations are formulated for water quality control, water resource monitoring, and measures for restoring ecological safety of aquatic ecosystems in the post-war period.
The study results are significant for developing scientifically grounded water resource management strategies and environmental protection policies supporting restoration and sustainable development of Ukraine’s aquatic ecosystems after the end of hostilities.
Chapter 24 “Mathematical Model for Assessing the Instability in Complex Systems Under Pandemics and Systemic Risks” (authors – researchers of Mathematical Methods of Operations Research Department of V.M. Glushkov Institute of Cybernetics NAS of Ukraine: Senior Researcher Candidate of Biological Sciences Kostyantyn Atoiev and Head of Department Corresponding Member Pavlo Knopov) develop a mathematical model for assessing the emergence and development of instability in complex socio-economic systems under pandemics and systemic risks.
This chapter proposes an approach based on analyzing system parameters’ proximity to bifurcation (transition) values using smooth function theory methods for quantitative risk assessment of crisis phenomena emergence in various economic sectors. The authors build a six-sector Lorenz-type model with variable coefficients, enabling integration of different economic branches into a single structure and analysis of their response to external shocks.
Modeling results allow forecasting development of crisis regimes, choosing strategies to ensure a given safety level, studying abrupt system behavior changes, ranking different threat types, and identifying “weak links” significantly influencing instability formation and safety space distortion. This approach is important for quantitative risk assessment and forming adaptive management strategies under uncertainty arising during pandemics, geopolitical upheavals, and systemic crises.
Chapter 25 “Comprehensive Analysis of Robust Preventive and Adaptive Measures for Managing Food, Energy, Water and Social Sectors in the Context of Systemic Risks and Consequences of COVID-19” (authors – researchers of Institute of Demography and Social Studies of NAS of Ukraine: Deputy Director for Scientific Work Doctor of Economics Mykhailo Khvesyk, Head of Spatial Development and Quality of Life Department Doctor of Economics, Professor Ihor Bystryakov, Head of Natural Resources and Environmental Safety Department Doctor of Economics, Professor Lyudmyla Levkovska, Head of Ecosystem Services and Protected Areas Department Doctor of Economics Anatoliy Sunduk, and Leading Researcher of Natural Resources and Environmental Safety Department Doctor of Economics Hanna Obikhod) comprehensively analyze preventive and adaptive management measures of food, energy, water, and social sectors in the context of systemic risks caused by the COVID-19 pandemic.
This chapter thoroughly investigates direct, indirect, and secondary consequences of pandemic control policies on the natural environment and functioning of intersectoral systems, emphasizing that restrictive measures (lockdowns, quarantine regimes) affected not only human health but also water resource structure, ecosystem services, water management institutions, and socio-economic dynamics.
The authors also justify a systemic approach to policy and measure development combining preventive (ex-ante) and adaptive (ex-post) actions to reduce vulnerability of sectoral systems, including institutional protection mechanisms and flexible response algorithms during crises. They identify key principles for creating road maps and institutional bases for systemic management of water resources, ecosystem services, and socio-economic processes in the post-pandemic period.
The chapter’s results are important for forming resilient strategies for managing food, energy, water, and social systems considering both global pandemic consequences and potential future systemic risks and can be used in policy development aimed at enhancing societal resilience in complex crisis conditions.
Chapter 26 “Consequences of COVID-19 and Systemic Risks as an Impetus for the Development of Robust Preventive and Adaptive Measures of the Governance of the Healthcare System of Ukraine” (authors – researchers of Institute of Demography and Social Studies NAS of Ukraine: Head of Demographic Modeling and Forecasting Department Doctor of Economics, Professor Dmytro Shushpanov, Leading Researcher of the same department Doctor of Economics Maryna Pugachova, and Deputy Director for Scientific Work Corresponding Member Oleksandr Hladun) analyze consequences of the COVID-19 pandemic and related systemic risks for healthcare functioning and governance in Ukraine and identify key preventive and adaptive measures to enhance resilience of this critical sector.
The chapter’s authors investigate structural problems in the healthcare system exacerbated by the pandemic, including medical staff shortages, uneven resource provision, and insufficient intersectoral coordination. Comparing strictness of state quarantine measures and COVID-19 progression, they assess excess mortality and epidemic impact on mortality cause structure, highlighting aspects weakening the system’s capacity to respond to global shocks.
The authors propose scientifically grounded recommendations to strengthen healthcare system adaptability and resilience, including improving legislative framework, optimizing human and material resource allocation, and developing mechanisms for operational intersectoral cooperation during crises. These measures aim to enhance the healthcare system’s ability to adequately confront future pandemic challenges and other major systemic risks arising in modern conditions.
Chapter 27 “Challenges Faced by Ukrainian Families During COVID-19 and the Government’s Response to Them” (authors – researchers of Department of Demographic Processes and Demographic Policy Studies of Institute of Demography and Social Studies NAS of Ukraine: Head of Department Doctor of Economics, Professor Iryna Kurylo and Leading Researchers Candidates of Economic Sciences Svitlana Aksyonova, Lyudmyla Slyusar, and Borys Krymer) study socio-economic challenges faced by Ukrainian families during the COVID-19 pandemic and assess government response to these challenges.
This chapter analyzes many aspects of everyday family life in Ukraine during the pandemic, including how quarantine restrictions and epidemiological risks affected economic situations, division of household duties, and gender roles. It shows that the main burden fell on women, who largely cared for children, organized distance learning, and maintained households during lockdowns, significantly exacerbating their socio-economic vulnerability.
The authors also consider problems of social support provision to families with children, elderly people, and persons with disabilities, including insufficient quality and coverage of relevant state programs. The impact of quarantine measures on domestic atmosphere is studied, accompanied by increased household conflicts, domestic violence cases, and difficulties accessing adequate living space during prolonged cohabitation.
The chapter’s final part evaluates the Ukrainian Government’s response to these challenges, including financial support and social programs, concluding their incomplete effectiveness and the need for developing comprehensive, targeted social protection measures to safeguard families and build a more resilient social environment amid pandemics and future crises.
Chapter 28 “Losses in the Quality of Life Under COVID-19” (authors – researchers of Population Quality of Life Research Department of Institute of Demography and Social Studies NAS of Ukraine: Head of Department Candidate of Economic Sciences Lyudmyla Cherenko and Senior Researchers Candidates of Economic Sciences Yuliya Klymenko and Anna Reut) analyze the impact of the COVID-19 pandemic on the quality of life of Ukraine’s population and increased poverty risk among various social groups, exacerbating existing socio-economic challenges.
This chapter systematically assesses how quarantine restrictions and income reductions affected welfare levels of different household categories, including those with unemployed family members, low education levels, or limited income sources. The authors show that a significant part of the population fell below the poverty line in 2020–2021 compared to hypothetical no-pandemic scenarios, indicating substantial socio-economic quality of life losses due to job cuts, income declines, and unequal access to digital technologies.
Special attention is paid to differentiated crisis impacts on various population groups—from large households to single parents and elderly pensioners, who suffered the worst consequences. The authors also consider gender differences in experiencing social pandemic effects, including changes in decision-making patterns and redistribution of social roles in society.
The chapter’s results contribute to better understanding social consequences of global crises for population quality of life, providing an important scientific basis for forming social protection policies, support programs for vulnerable groups, and adaptive strategies during overcoming pandemic consequences and other large systemic risks.
According to information from the Committee on Systems Analysis of the NAS of Ukraine